The use of Machine Learning is growing at an extraordinary rate. In fact, business leaders said they believe Artificial Intelligence (AI) is going to be fundamental in the future according to a PwC study. 72 percent termed it a “business advantage.” There’s no denying the cost savings and efficiencies Machine Learning can provide. However, researchers still seek to perfect and appropriately apply data and technology. Professional translators also incorporate a form of Machine Learning into practice. This is known as machine translation.
What is machine translation?
Machine translation utilizes software to translate one language into another. The process performs simple substitutions of words with no human involvement. One of the most well-known examples is Google Translate. Google CEO Sundar Pichai revealed that their service now translates 143 billion words a day. While highly popular, professional translators agree that Google Translate lacks accuracy. The final translations that Google produces, especially when cultural references are involved, are not precise.
What are the different types of machine translations?
There are three main types of machine translations. The first is rule-based. The translation relies on a collection of language “rules” developed by linguists. With countless linguist guidelines, rule-based machine translation requires costly upkeep. First of its kind available commercially, today the technology has since been replaced with more efficient software.
The second type is statistical machine translation. This more complex form uses algorithms to produce text selected from millions of possible permutations. In some situations, combining rule-based and statistical translations improves the quality of the translation. Similar to rule-based, this form is not being used as frequently due to the additional work needed to maintain the system.
The third type and most commonly known is neural machine translation. First introduced by Google, neural machine translation uses an AI modeled after the human brain to predict a sequence of words. This interactive form allows translators to train the machine in real time as they rework and edit suggested phrases. The engine will learn and remember new terms in the correct context and tone for greater quality in future translations. Sentences and phrases generated from a neural network-based machine translation usually sound more natural and fluent.
When should I use machine translation?
While machine translation may optimize the speed, many projects require more attention. As mentioned earlier with Google Translate, machine translation lacks the ability to fully understand culture, context, and tone. Translation errors and fluency issues are still possible. Sales, legal, life science, safety, and marketing content should be handled by human translators. There are, however, certain contexts and situations where it is most beneficial. One scenario is having large volumes of content to translate with short deadlines. Another instance machine translation can be applied is as a placeholder while human translation is in process.
Translators across the industry can agree, it is highly recommended that machine translated content should undergo human post-editing. Post-editing can be light. The translator ensures the text is accurate and understandable. Post-editing can also be more in-depth or full. The translator ensures the text is accurate, fluent, and consistent with the target language.
Machine translation has grown more sophisticated over the years. Nonetheless, it’s still imperative to have a human translator check for errors. Especially if the translation is for professional use. Every translation mistake has the potential to drive away customers or worse…go viral.
